Practical and validated tool to assess falls risk in the primary care setting:A systematic review

Objective: Although several falls risk assessment tools are available, it is unclear which have been validated and which would be most suitable for primary care practices. This systematic review aims to identify the most suitable falls risk assessment tool for the primary care setting (ie, requires limited time, no expensive equipment and no additional space) and that has good predictive performance in the assessment of falls risk among older people living independently. Design: A systematic review based on prospective studies. Methods: An extensive search was conducted in the following databases: PubMed, Embase, CINAHL, Cochrane and PsycINFO. Tools were excluded if they required expensive and/or advanced software that is not usually available in primary care units and if they had not been validated in at least three different studies. Of 2492 articles published between January 2000 and July 2020, 27 were included. Results: Six falls risk assessment tools were identified: Timed Up and Go (TUG) test, Gait Speed test, Berg Balance Scale, Performance Oriented Mobility Assessment, Functional Reach test and falls history. Most articles reported area under the curve (AUC) values ranging from 0.5 to 0.7 for these tools. Sensitivity and specificity varied substantially across studies (eg, TUG, sensitivity:10%–83.3%, specificity:28.4%–96.6%). Conclusions: Given that none of the falls risk assessment tools had sufficient predictive performance (AUC <0.7), other ways of assessing high falls risk among independently living older people in primary care should be investigated. For now, the most suitable way to assess falls risk in the primary care setting appears to involve asking patients about their falls history. Compared with the other five tools, the falls history requires the least amount of time, no expensive equipment, no training and no spatial adjustments. The clinical judgement of healthcare professionals continues to be most important, as it enables the identification of high falls risk even for patients with no falls history. Trial registraion number: The Netherlands Trial Register, NL7917; Pre-results

Adverse health effects after breast cancer up to 14 years after diagnosis

BACKGROUND: The number of breast cancer survivors increases, but information about long-term adverse health effects in breast cancer survivors is sparse. We aimed to get an overview of the health effects for which survivors visit their general practitioner up to 14 years after diagnosis. METHODS: We retrieved data on 11,671 women diagnosed with breast cancer in 2000–2016 and 23,242 age and sex matched controls from the PSCCR-Breast Cancer, a database containing data about cancer diagnosis, treatment and primary healthcare. We built Cox regression models for 685 health effects, with time until the health effect as the outcome and survivor/control and cancer treatment as predictors. Models were built separately for four age groups (aged 18/44, 45/59, 60/74 and 75/89) and two follow-up periods (1/4 and 5/14 years after diagnosis). RESULTS: 229 health effects occurred statistically significantly more often in survivors than in controls (p < 0.05). Health effects varied by age, time since diagnosis and treatment, but coughing, respiratory and urinary infections, fatigue, sleep problems, osteoporosis and lymphedema were statistically significantly increased in breast cancer survivors. Osteoporosis and chest symptoms were associated with hormone therapy; respiratory and skin infections with chemotherapy and lymphedema and skin infections with axillary dissection. CONCLUSIONS: Breast cancer survivors may experience numerous adverse health effects up to 14 years after diagnosis. Insight in individual risks may assist healthcare professionals in managing patient expectations and improve monitoring, detection and treatment of adverse health effects

Follow-up of curatively treated cancer in primary care:a qualitative study of the views of Dutch GPs

BACKGROUND: Follow-up for cancer typically occurs in secondary care, and improved survival has increased demands on these services. Other care models may alleviate this burden, such as moving (parts of) follow-up care for curatively treated patients from secondary to primary care (care substitution). AIM: To explore the opinions of GPs regarding the potential benefits, barriers, and requirements of care substitution for breast and colorectal cancer. DESIGN AND SETTING: A qualitative study of the opinions of purposively sampled GPs in Dutch primary care. METHOD: Focus group sessions and individual semi-structured interviews were recorded and transcribed verbatim. Data were analysed by two independent researchers using thematic analysis. RESULTS: Two focus groups (n = 14) were conducted followed by nine individual interviews. Three main themes were identified: perceived benefits, perceived barriers, and perceived requirements. Perceived benefits included better accessibility and continuity of care, and care closer to patients' homes. Uncertainty about cancer-related competences and practical objections were perceived as barriers. Requirements included close specialist collaboration, support from patients for this change, and stepwise implementation to avoid loss of existing care quality. CONCLUSION: Most GPs reported that they were not in favour of complete care substitution, but that primary care could have greater formal involvement in oncological follow-up if there is close collaboration with secondary care (that is, shared care), support from patients, sufficient resource allocation, stepwise implementation with clear guidelines, and monitoring of quality. Clear and broadly supported protocols need to be developed and tested before implementing follow-up in primary care

Reliably estimating prevalences of atopic children: An epidemiological study in an extensive and representative primary care database

Electronic health records stored in primary care databases might be a valuable source to study the epidemiology of atopic disorders and their impact on health-care systems and costs. However, the prevalence of atopic disorders in such databases varies considerably and needs to be addressed. For this study, all children aged 0-18 years listed in a representative primary care database in the period 2002-2014, with sufficient data quality, were selected. The effects of four different strategies on the prevalences of atopic disorders were examined: (1) the first strategy examined the diagnosis as recorded in the electronic health records, whereas the (2) second used additional requirements (i.e., the patient had at least two relevant consultations and at least two relevant prescriptions). Strategies (3) and (4) assumed the atopic disorders to be chronic based on strategy 1 and 2, respectively. When interested in cases with a higher probability of a clinically relevant disorder, strategy 2 yields a realistic estimation of the prevalence of atopic disorders derived from primary care data. Using this strategy, of the 478,076 included children, 28,946 (6.1%) had eczema, 29,182 (6.1%) had asthma, and 28,064 (5.9%) had allergic rhinitis; only 1251 (0.3%) children had all three atopic disorders. Prevalence rates are highly dependent on the clinical atopic definitions used. The strategy using cases with a higher probability of clinically relevant cases, yields realistic prevalences to establish the impact of atopic disorders on health-care systems. However, studies are needed to solve the problem of identifying atopic disorders that are missed or misclassified

Risks for comorbidity in children with atopic disorders: an observational study in Dutch general practices

OBJECTIVE: This study aimed to investigate both atopic and non-atopic comorbid symptoms and diseases in children with physician-diagnosed atopic disorders (atopic eczema, asthma and allergic rhinitis).METHODS: All children aged 0-18 years listed in a nationwide primary care database (the Netherlands Institute for Health Services Research-Primary Care Database) with routinely collected healthcare data in 2014 were selected. Children with atopic disorders were matched on age and gender with non-atopic controls within the same general practice. A total of 404 International Classification of Primary Care codes were examined. Logistic regression analyses were performed to examine the associations between the presence of atopic disorders and (non-)atopic symptoms and diseases by calculating ORs.RESULTS: Having one of the atopic disorders significantly increased the risk of having other atopic-related symptoms, even if the child was not registered as having the related atopic disorder. Regarding non-atopic comorbidity, children with atopic eczema (n=15 530) were at significantly increased risk for (infectious) skin diseases (OR: 1.2-3.4). Airway symptoms or (infectious) diseases (OR: 2.1-10.3) were observed significantly more frequently in children with asthma (n=7887). Children with allergic rhinitis (n=6835) had a significantly distinctive risk of ear-nose-throat-related symptoms and diseases (OR: 1.5-3.9). Neither age nor gender explained these increased risks.CONCLUSION: General practitioners are not always fully aware of relevant atopic and non-atopic comorbidity. In children known to have at least one atopic disorder, specific attention is required to avoid possible insufficient treatment and unnecessary loss of quality of life

Falls prevention at GP practices:A description of daily practice

BACKGROUND: General practitioners (GPs) can be considered the designated professionals to identify high fall risk and to guide older people to fall preventive care. Currently it is not exactly known how GPs treat this risk. This study aims to investigate GPs’ daily practice regarding fall preventive care for frail older patients. METHODS: Sixty-five GPs from 32 Dutch practices participated in this study for a period of 12 months. When a GP entered specific International Classification of Primary Care-codes related to frailty and/or high fall risk in their Electronic Health Records, a pop-up appeared asking “Is this patient frail?”. If the GP confirmed this, the GP completed a short questionnaire about patient’s fall history and fear of falling (FOF), and the fall preventive care provided. RESULTS: The GPs completed questionnaires regarding 1394 frail older patients aged ≥75. Of 20% of these patients, the GPs did not know whether they had experienced a fall or not. The GPs did not know whether a FOF existed in even more patients (29%). Of the patients with a fall history and/or a FOF (N = 726), 37% (N = 271) received fall preventive care. Two main reasons for not offering fall preventive care to these patients were: I) the patient finds treatment too intensive or too much of a hassle (37%), and II) the GP identified a high fall risk but the patient did not acknowledge this (14%). When patients were treated for high fall risk, the GP and the physiotherapist were the most frequently involved health care providers. The involved health care providers most often treated mobility limitations, cardiovascular risk factors, and FOF. CONCLUSIONS: The results from this study show that GPs were frequently not aware of their frail patient’s fall history and/or FOF and that the majority of the frail older patients with a fall history and/or FOF did not receive fall preventive care. Developing systematic screening strategies for the primary care setting enhancing the identification of high fall risk and the provision of fall preventive care may improve patients’ quality of life and reduce health care costs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12875-021-01540-7

Development of machine learning models to predict cancer-related fatigue in Dutch breast cancer survivors up to 15 years after diagnosis

Purpose: To prevent (chronic) cancer-related fatigue (CRF) after breast cancer, it is important to identify survivors at risk on time. In literature, factors related to CRF are identified, but not often linked to individual risks. Therefore, our aim was to predict individual risks for developing CRF.Methods: Two pre-existing datasets were used. The Nivel-Primary Care Database and the Netherlands Cancer Registry (NCR) formed the Primary Secondary Cancer Care Registry (PSCCR). NCR data with Patient Reported Outcomes Following Initial treatment and Long-term Evaluation of Survivorship (PROFILES) data resulted in the PSCCR-PROFILES dataset. Predictors were patient, tumor and treatment characteristics, and pre-diagnosis health. Fatigue was GP-reported (PSCCR) or patient-reported (PSCCR-PROFILES). Machine learning models were developed, and performances compared using the C-statistic.Results: In PSCCR, 2224/12813 (17%) experienced fatigue up to 7.6 ± 4.4 years after diagnosis. In PSCCR-PROFILES, 254 (65%) of 390 patients reported fatigue 3.4 ± 1.4 years after diagnosis. For both, models predicted fatigue poorly with best C-statistics of 0.561 ± 0.006 (PSCCR) and 0.669 ± 0.040 (PSCCR-PROFILES).Conclusion: Fatigue (GP-reported or patient-reported) could not be predicted accurately using available data of the PSCCR and PSCCR-PROFILES datasets.Implications for Cancer Survivors: CRF is a common but underreported problem after breast cancer. We aimed to develop a model that could identify individuals with a high risk of developing CRF, ideally to help them prevent (chronic) CRF. As our models had poor predictive abilities, they cannot be used for this purpose yet. Adding patient-reported data as predictor could lead to improved results. Until then, awareness for CRF stays crucial

Diagnostic accuracy of follow-up tests for detecting colorectal cancer recurrences in primary care:A systematic review and meta-analysis

Introduction Traditionally, follow-up of colorectal cancer (CRC) is performed in secondary care. In new models of care, the screening part care could be replaced to primary care. We aimed to synthesise evidence on the diagnostic accuracy of commonly used screeners in CRC follow-up applicable in primary care: carcinoembryonic antigen (CEA), ultrasound and physical examination. Methods Medline, EMBASE, Cochrane Trial Register and Web of Science databases were systematically searched. Studies were included if they provided sufficient data for a 2 × 2 contingency tables. QUADAS-2 was used to assess methodological quality. We performed bivariate random effects meta-analysis, generated a hypothetical cohort, and reported sensitivity and specificity. Results We included 12 studies (n = 3223, median recurrence rate 19.6%). Pooled estimates showed a sensitivity for CEA (≤ 5 μg/l) of 59% [47%–70%] and a specificity of 89% [80%–95%]. Only few studies reported sensitivities and specificities for ultrasound (36–70% and 97–100%, respectively) and clinical examination (23% and 27%, respectively). Conclusion In practice, GPs could perform CEA screening. Radiological examination in a hospital setting should remain part of the surveillance strategy. Personalised algorithms accounting for recurrence risk and changes of CEA-values over time might add to the diagnostic value of CEA in primary care